The present invention generally relates to the field of image reconstruction in X-ray tomosynthesis systems, and more particularly to the direct reconstruction of tomosynthesis images.
In three-dimensional (3D) X-ray imaging techniques, such as X-ray tomosynthesis, projection images are acquired for varying positions of one or more X-ray sources relative to the imaged object. For example, in X-ray tomosynthesis, X-rays are generated by the one or more X-ray sources, and are generally collimated prior to passing through the object being scanned. The attenuated X-rays are then detected by a set of detector elements. Each detector element produces a signal based on the intensity of the attenuated X-rays, and the resulting signals are processed to produce the projection images. From these projection images, a three-dimensional volumetric image of the imaged object is reconstructed. Typically, the reconstructed volumetric image is arranged in slices that are generally parallel to the detector plane.
In tomosynthesis imaging, it is typical to acquire the projection radiographs, i.e., images, from only a few angles within a relatively narrow angular range of the X-ray source relative to the imaged object. Despite the narrow range from which projection images are acquired, it is still generally possible to reconstruct a three dimensional representation of all or part of the imaged volume. In general, some of the challenges that need to be addressed by any tomosynthesis reconstruction technique are efficient separation of overlying tissue, enhancement of contrast, particularly of small structures, and artifact minimization. However, due to the limited or incomplete data acquired in tomosynthesis, a perfect reconstruction in the mathematical sense is not possible. As a result, the volumetric images reconstructed from a tomosynthesis acquisition may exhibit artifacts, for example due to high-contrast structures in the imaged volume.
Direct reconstruction techniques used in tomosynthesis, such as reconstruction via shift-and-add algorithms or simple backprojection, are generally fast and computationally efficient, since they allow reconstruction of a three-dimensional image data set in a single reconstruction step. They also allow for reconstruction of only small sub volumes of the imaged volume. Unfortunately, most direct reconstruction methods exhibit relatively poor image quality with a low contrast and a significant artifact level. Other reconstruction techniques, such as algebraic reconstruction techniques (ART) improve image quality through an iterative step. In particular, these types of iterative reconstruction techniques typically perform an initial reconstruction followed by iterative updates of the three-dimensional image data set until some threshold criteria is met.
However, iterative reconstruction techniques may be computationally expensive since they generally involve reconstructing a three-dimensional image of the full imaged volume, and not just a subvolume. In addition, they generally iteratively update the full three-dimensional image, typically with at least five to ten iterations or more, so that substantial computational effort may be required.